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Thursday, February 19, 2009

Fingerprint sensors

Fingerprint sensors
I have been discussing on fingerprint authentication system. There are different types of fingerprint sensors used for Fingerprint authentication

Static capacitive Type 1 & 2
Dynamic capacitive
Luminescent capacitive
Optical reflexive
Optical transmissive with fiber optic plate
Optical contactless
Acoustic (ultrasound)
Pressure sensitive
Thermal line
Capacitive and optical line
The main features of all these fingerprint sensors are to try to generate a digital picture of the finger surface. This picture normally has a pixel resolution of 500 dpi. The picture generation can be different for every type of sensor. The various types of fingerprint sensors and its functions are described here.

Static Capacitive Sensor Type 1
In this type, one electrode is responsible for each pixel and measures the capacity compared to the neighbor electrode/pixel (inter pixel measurement). The capacity, in turn, is dependent on the dielectric. If a pixel is on a groove (i.e. air), the capacity is substantially smaller than on a finger line (ridge). In this case, the dielectric is water, which is distinguished by a very high dielectric
constant. The measurement of capacity is static in the sense that charging happens with fixed charge units and then voltage is measured.
Static Capacitive Sensor Type 2
In this type, One electrode per pixel is used, but the capacity is measured between pixel and ground, whereby the conductivity of the fingers does not play an insignificant role. The capacity measurement is in principle the same as in type 1.
Dynamic Capacitive Sensor
Here the capacity is measured by AC voltage. Inter pixel and pixel to ground measures can also be used in this type.
Luminescent Capacitive Sensor
An electroluminescent foil with a transparent back electrode uses the finger at its front side as counter electrode. At the points where the finger ridges touch the foil surface, the field strength is largest, as a result of this, the light emission brightest. That way a glowing image of the ridge structure develops at the back side of the foil.
Optical Reflexive Sensor
Here, the finger lies on a prism surface. Where the finger ridges touch the glass, a total reflection of light inside of the glass is disturbed. This will supply a picture of the finger lines to a camera chip.
Optical Transmissive Sensors with fiber optical plate
Here a suitable light source illuminates through the finger. The finger lies directly on a fiber optical plate, which, in turn is directly connected to a camera chip. The fiber optical plate ensures that the finger does not touch the camera chip, nevertheless the light arrives at the camera chip without losing focus.
Optical Contactless Sensor
The finger surface is directly acquired by a camera chip. The fingerprint area needs no contact to a plate.

Acoustic (Ultrasound) Sensors
Here a picture of the finger surface on the glass is recorded by very high frequency ultrasound (The frequency is order of 50 MHz).

Pressure Sensitive Sensors
With pressure sensors, the pressure per pixel of the finger is measured.

Thermal Line Sensors
With these sensors, the finger is moved linearly over a narrow array of thermal sensors, similar to sensors for opening automatic doors on a larger scale. The thermal sensors register temperature differences over time, which vary between the finger lines and grooves.

Capacitive and Optical Line Sensors
These sensor arrays work similar to thermal line sensors. Instead of temperature differences of time, the single sensors cells measure the capacity or the light, respectively, to build the image.
The fingerprint recognition is based on the minutiae. Successively recorded fingerprints are never identical, rather are at best highly 'similar' due to differences in finger position, application pressure, finger angle, dirtiness, and the physiological constitution of the user. The measure of similarity is given a score. The higher this score, the more similar the fingerprint, and vice versa. During the matching process in minutia based systems, one tries to minimize the influence of positioning and angle discrepancy, and incidentally size variations (in order to calculate out the effects of growth until around 18 years). The actual picture is adjusted and rotated with respect
to the reference picture until the distance between minutia is minimized.

The resulting similarity score, then depends on the following:
Number of minutia in agreement
Exactness of the positioning agreement
Degree of agreement of the minutia directions
Type of minutia agreement (line ending versus branching)
All values will be weighted with the picture quality near a minutia

Basically one can say that few, but very strongly matching minutia can receive a similar score as a case with many, but weakly matching minutia.
I think you are ready to start your project on Fingerprint authentication system

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